# coding: latin-1
"""
This is a Brian script implementing a benchmark described
in the following review paper:

Simulation of networks of spiking neurons: A review of tools and strategies (2007).
Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe,
Natschläger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller,
Davison, El Boustani and Destexhe.
Journal of Computational Neuroscience 23(3):349-98

Benchmark 2: random network of integrate-and-fire neurons with exponential synaptic currents

Clock-driven implementation with exact subthreshold integration
(but spike times are aligned to the grid)

R. Brette - Oct 2007
--------------------------------------------------------------------------------------
Brian is a simulator for spiking neural networks written in Python, developed by
R. Brette and D. Goodman.
http://brian.di.ens.fr
"""

from brian import *
import time

start_time=time.time()
taum=20*ms
taue=5*ms
taui=10*ms
Vt=-50*mV
Vr=-60*mV
El=-49*mV

eqs= Equations('''
dv/dt  = (ge+gi-(v-El))/taum : volt
dge/dt = -ge/taue : volt
dgi/dt = -gi/taui : volt
''')

P=NeuronGroup(4000,model=eqs,threshold=Vt,reset=Vr,refractory=5*ms)
P.v=Vr
P.ge=0*mV
P.gi=0*mV

Pe=P.subgroup(3200)
Pi=P.subgroup(800)
Ce=Connection(Pe,P,'ge')
Ci=Connection(Pi,P,'gi')
we=(60*0.27/10)*mV # excitatory synaptic weight (voltage)
wi=(-20*4.5/10)*mV # inhibitory synaptic weight
Ce.connect_random(Pe, P, 0.02,weight=we)
Ci.connect_random(Pi, P, 0.02,weight=wi)
P.v=Vr+rand(len(P))*(Vt-Vr)

# Record the number of spikes
Me=PopulationSpikeCounter(Pe)
Mi=PopulationSpikeCounter(Pi)
# A population rate monitor
M = PopulationRateMonitor(P)

print "Network construction time:",time.time()-start_time,"seconds"
print len(P),"neurons in the network"
print "Simulation running..."
run(1*msecond)
start_time=time.time()

run(1*second)

duration=time.time()-start_time
print "Simulation time:",duration,"seconds"
print Me.nspikes,"excitatory spikes"
print Mi.nspikes,"inhibitory spikes"
plot(M.times/ms,M.smooth_rate(2*ms,'gaussian'))
show()
